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Record W3088069114 · doi:10.1126/scitranslmed.aax2992

Transplanted microvessels improve pluripotent stem cell–derived cardiomyocyte engraftment and cardiac function after infarction in rats

2020· article· en· W3088069114 on OpenAlexafffund
Xuetao Sun, Jun Wu, Beiping Qiang, Rocco Romagnuolo, Mark Gagliardi, Gordon Keller, Michael A. Laflamme, Ren‐Ke Li, Sara S. Nunes

Bibliographic record

VenueScience Translational Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsHeart and Stroke FoundationUniversity of TorontoToronto General HospitalUniversity Health Network
FundersCanadian Institutes of Health Research
KeywordsInduced pluripotent stem cellTransplantationStem cellCardiologyMedicineMyocardial infarctionRegenerative medicinePerfusionRegeneration (biology)LimitingInternal medicineCell biologyEmbryonic stem cellBiology

Abstract

fetched live from OpenAlex

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) offer an unprecedented opportunity to remuscularize infarcted human hearts. However, studies have shown that most hiPSC-CMs do not survive after transplantation into the ischemic myocardial environment, limiting their regenerative potential and clinical application. We established a method to improve hiPSC-CM survival by cotransplanting ready-made microvessels obtained from adipose tissue. Ready-made microvessels promoted a sixfold increase in hiPSC-CM survival and superior functional recovery when compared to hiPSC-CMs transplanted alone or cotransplanted with a suspension of dissociated endothelial cells in infarcted rat hearts. Microvessels showed unprecedented persistence and integration at both early (~80%, week 1) and late (~60%, week 4) time points, resulting in increased vessel density and graft perfusion, and improved hiPSC-CM maturation. These findings provide an approach to cell-based therapies for myocardial infarction, whereby incorporation of ready-made microvessels can improve functional outcomes in cell replacement therapies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.228
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations91
Published2020
Admission routes2
Has abstractyes

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